Rein Agent Risk Scale
RemoteAbout Rein Agent Risk Scale
Rein Agent Risk Scale provides a self-check that assigns an indicative A–D rating to controls around agents that can spend money. It includes MCP examples for Python and agent frameworks; ratings are self-declared and unverified.
Key features
- 01Self-check based on structured answers
- 02Published fixed scoring rules
- 03Findings and steps to improve a grade
- 04MCP examples for several frameworks
- 05Pseudonymised records by default
Use cases
- 01Assess controls around an agent that can spend money
- 02Inspect which controls affect an indicative rating
- 03Connect an agent framework to the self-check
MCP support
The MCP endpoint exposes `self_check`, `get_scale`, and `explain_grade` tools over streamable HTTP without sign-in. Examples show connections using the Python SDK, OpenAI Agents SDK, LangChain/LangGraph, and CrewAI.
Configuration
Remote servers need no local install. Pick your client, then copy the config or install in one click.
https://rein-scale.mangowave-ad191d9c.eastus.azurecontainerapps.io/mcpclaude mcp add --transport http reinlayer-rein-agent-risk-scale https://rein-scale.mangowave-ad191d9c.eastus.azurecontainerapps.io/mcp{"mcpServers":{"reinlayer-rein-agent-risk-scale":{"type":"http","url":"https://rein-scale.mangowave-ad191d9c.eastus.azurecontainerapps.io/mcp"}}}Open in Cursor{"servers":{"reinlayer-rein-agent-risk-scale":{"type":"http","url":"https://rein-scale.mangowave-ad191d9c.eastus.azurecontainerapps.io/mcp"}}}Open in VS Code{"mcpServers":{"reinlayer-rein-agent-risk-scale":{"type":"http","url":"https://rein-scale.mangowave-ad191d9c.eastus.azurecontainerapps.io/mcp"}}}{"mcpServers":{"reinlayer-rein-agent-risk-scale":{"serverUrl":"https://rein-scale.mangowave-ad191d9c.eastus.azurecontainerapps.io/mcp"}}}Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.
Tools
3This tool takes no parameters.
Details
From the project READMERein Agent Risk Scale: MCP examples
Copy-paste examples that connect an AI agent to the Rein Agent Risk Scale self-check over MCP. The self-check gives an agent that can spend money an indicative Agent Risk Rating on an A–D scale (NR = not rated), from the controls around it: who is accountable, spend caps and where they are enforced, payee limits, a stop switch and who holds it, human escalation, monitoring, history and incidents.
MCP endpoint (streamable HTTP, no sign-in):
https://rein-scale.mangowave-ad191d9c.eastus.azurecontainerapps.io/mcp
The endpoint has three tools:
self_checkscores a deployment from structured answers;get_scalereturns the A–D scale (NR = not rated) and what each grade means;explain_gradesays what a grade requires.
Scoring is fixed, published rules with no language model, so the same answers always give the same grade (methodology 0.3.1). Harness memory counts in your favour within your grade's band; it never moves the letter.
Self-declared and unverified; not a credit rating.
Examples
Each example is one file of under 40 lines.
| file | framework | install (tested versions) |
|---|---|---|
examples/plain_mcp.py | the mcp Python SDK, no LLM | pip install "mcp==2.3.0" |
examples/openai_agents.py | OpenAI Agents SDK | pip install "openai-agents==0.23.1" |
examples/langgraph_agent.py | LangChain / LangGraph with the MCP adapters | pip install "langchain==1.4.3" "langchain-openai==1.6.7" "langchain-mcp-adapters==0.3.2" |
examples/crewai_agent.py | CrewAI | pip install "crewai==1.15.23" "crewai-tools[mcp]==1.15.23" |
The three agent examples use OpenAI by default (export OPENAI_API_KEY=...). Pass any model your framework accepts to main() to use another.
- Tested: every example was run against the live endpoint on 7 October 2026 at the versions above.
- Watched weekly: a check runs them against the latest framework releases, because releases rename things.
mcp2.x renamed its HTTP client, and it now yields two streams instead of three.
Use it from any MCP client
{
"mcpServers": {
"rein-agent-risk-scale": {
"type": "http",
"url": "https://rein-scale.mangowave-ad191d9c.eastus.azurecontainerapps.io/mcp"
}
}
}
There are docs written for agents at /skill.md and /llms.txt.
What a self-check is, and is not
- Indicative. The grade comes from your own answers. It is self-declared and unverified, so the rated grade stays
NR(not rated) until the controls are verified. - Explained. The response lists the findings that most explain the grade and the steps that would raise it.
- Private by default. Answers and grades are stored under a pseudonymised record. A handle is published only if you set
publish_opt_in: true. Never send secrets or personal data. - Not a credit rating, not investment advice, and not an offer of credit.
License
MIT. See LICENSE.
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.